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aethronai
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Model Details
Model Provider
aethronai
Model Tree
Qwen/Qwen3-30B-A3B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
| Base model | Qwen/Qwen3-30B-A3B-Instruct-2507, bf16 |
| Method | LoRA, rank 16, alpha 32, dropout 0.05, on the attention projections q, k, v, o (experts and router stay frozen), merged into the base weights |
| Data | AethronAI agent transcripts, up to 8,192 tokens each, about 10% held out by address |
| Loss | assistant turns only (tool calls and the final answer, ~9% of tokens) |
| Schedule | 2 epochs, learning rate 2e-4 cosine, effective batch 8 |
Data. Questions in English, Spanish, German, French, Portuguese, Russian, Chinese, Japanese and Korean about
real tokens, wallets and transactions on Robinhood Chain, Base, BNB Smart Chain, Arc and Solana were run through
the real AethronAI agent with live chain data. The teacher was an open-weights model
(Qwen/Qwen3.8-27B through Hugging Face Inference Providers). A transcript was kept only if every lookup
succeeded, the model answered through final_answer without a reminder, and the evidence validator rejected at
most one statement (which was then removed). No Claude output was used.
The same data trained aethron-20b, a LoRA adapter for gpt-oss-20b.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("aethronai/aethron-30b")model = AutoModelForCausalLM.from_pretrained("aethronai/aethron-30b", dtype=torch.bfloat16, device_map="auto")Prompts use the Qwen3 chat template with AethronAI's tools (
text
tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True)hermes tool-call parser.